Scenario C: Decision Trees and EnsemblesYou train a decision tree classifier for churn with different maximum depths.You observe the following test performance: Depth 2: Accuracy 0.78, Recall(churn) 0.30 Depth 6: Accuracy 0.82, Recall(churn) 0.40 Depth 20: Accuracy 0.80, Recall(churn) 0.28 If a tree splits first on tenure_months at 3 months, the best interpretation is:

Scenario B: Customer Churn Classification A subscription business wants to predict whether a customer will churn (cancel) next month. Target: churn (1 = churned, 0 = stayed). The business cares more about catching likely churners than about occasionally flagging a loyal customer. A model has high accuracy but low recall on churners. Most likely issue?

Scenario A: Messy Retail Sales ExtractYou are analyzing a retail dataset with columns: date (string like “2025-03-01”) region (text with inconsistent capitalization and extra spaces) channel (“Online” or “Store”) price (numeric, may contain missing values) quantity (integer) Assume each row is an order line. You will clean the data and compute KPIs.You plot a histogram of revenue and see a long right tail. What does that typically indicate?

Scenario A: Messy Retail Sales ExtractYou are analyzing a retail dataset with columns: date (string like “2025-03-01”) region (text with inconsistent capitalization and extra spaces) channel (“Online” or “Store”) price (numeric, may contain missing values) quantity (integer) Assume each row is an order line. You will clean the data and compute KPIs.You want total revenue by region. Which expression is best?

Choose one of Jivhan’s workout symptoms and explain how the value of the chosen vital (from previous answer) contributes to the chosen symptom. 

Scenario B: Customer Churn Classification A subscription business wants to predict whether a customer will churn (cancel) next month. Target: churn (1 = churned, 0 = stayed). The business cares more about catching likely churners than about occasionally flagging a loyal customer. If the business wants to catch churners, which error is typically worse?

Scenario D: Revenue Prediction (Regression)A business predicts weekly revenue using features like ad_spend, number_of_customers, and average_discount.Two models are evaluated on a held-out test set: Model A: R² = 0.62, RMSE = 18,000 Model B: R² = 0.58, RMSE = 16,000 Lower RMSE is better. Higher R² is better.In multiple regression, the coefficient on ad_spend is best interpreted as:

Disengagement from the effort to reach a personal goal is always maladaptive or bad (according to the self-regulation perspective).

Scenario B: Customer Churn ClassificationA subscription business wants to predict whether a customer will churn (cancel) next month.Target: churn (1 = churned, 0 = stayed).The business cares more about catching likely churners than about occasionally flagging a loyal customer.In plain business language, a churn model output of 0.72 for a customer usually means:

The system concept level of a goal hierarchy resembles the concept of self-actualization or ideal self-image.